Trevor Brokowski

Director of Innovation @ LiGHT

San Diego, California, United States

About

Directing innovation

Experience

  • Director of Innovation at Laboratory for intelligent Global Health and Humanitarian Response Technologies (LiGHT)
    Jun 2026 - Present · 2 mos

  • Fellow at Bioethics International
    Jan 2024 - Apr 2026 · 2 yrs 4 mos

    Researched and advised on pharmaceutical companies transparency measures, and provided expertise and guidance on ethical frameworks for the development and implementation of AI

  • AI Fellow at Ministry of Health, Republic of Rwanda
    Apr 2025 - Dec 2025 · 9 mos

    Served as an AI Fellow with the Rwanda Ministry of Health’s Health Intelligence Centre, providing expertise in AI governance, evaluation, standards, and technical implementation. Contributed as a founding member to the development of Rwanda’s National Health AI Laboratory.

  • AI Researcher at David Geffen School of Medicine at UCLA
    Mar 2022 - Jul 2023 · 1 yr 5 mos

    Using AI and Machine Learning to analyze big data in healthcare, expand the scope of personalized intervention, medicine, and treatment, and perform deep phenotyping to identify and predict diagnoses and procedures. We do this with an emphasis on interpretability and usability with the goal of deployment in healthcare settings My current research is focused on preventing antimicrobial resistance and antibiotic resistant super-bugs, an event that is predicted to have a total death amount of over 50 million by 2050. Leveraging Deep Learning, we can use a patient's entire EHR record to construct personalized antibiograms and predict what antibiotics will cure the patients ailment. Thus we can minimize the prescription of broad range antibiotics in favor of personalized and directed treatments.

  • Quantum Machine Learning Researcher at Quantum Biology Tech (QuBiT) Lab @UCLA
    Sep 2021 - Jul 2023 · 1 yr 11 mos

    Using quantum machine learning to investigate and probe biological systems and processes. Selected to present at the IEEE International Conference for Quantum Computing and Engineering. My research has advanced the field of spin-system simulations by developing a novel method utilizing quantum machine learning to simulate the radical pair mechanism on a quantum device. We found that noisy optimization algorithms such as the simultaneous perturbation stochastic approximation (SPSA) are faster, more accurate, and immune to local minima when used in a hybrid quantum protocol. Scaling up this procedure could pave the way for us to eventually simulate larger and more physically relevant spin systems, going beyond the capabilities of current state of the art classical simulation schemes. Taught intro to quantum computing classes at UCLA.